The following account of the prior art relates to one of the areas of application of the present application, hearing aids.
Noise reduction systems in hearing aids traditionally consist of two signal processing components:
i) a spatial noise reduction component (beamformer) which tries to emphasize signals originating from a specific direction, typically the front direction, assuming the hearing aid user “chooses with the nose”, and
ii) a single-channel noise reduction system which often operates on the output of the directional noise reduction system, attempting to suppress the noise remaining in the signal.
The spatial noise reduction system often makes use of several fixed (=predetermined) beamformers. By using fixed beamformer building blocks in a time-varying manner, the actual beamformer system may still be time-varying/adaptive. The fixed beamformers are typically implemented by forming linear combinations of the input (microphone) signals. The problem to be solved in the present disclosure is essentially how to determine the coefficients of these linear combinations, i.e., the fixed beamformer weights, in a manner, which is optimized to the physical characteristics of the hearing aid user.
The fixed (or predetermined) beamformer weights may be determined off-line so that they realize certain prescribed spatial filtering characteristics. For example, in a minimum variance distortion-less response (MVDR) beamformer, it is desirable to implement a beam pattern, which has a gain of 0 dB in a certain direction, e.g., the frontal. In other cases, it is of interest to implement a target-cancelling beamformer, i.e., a beamformer which has a spatial null in the direction of the target, see e.g. [Kjems&Jensen; 2012] for an example.
Truly optimal beamformer weights are a function of several factors, including the physical characteristics of the user such as head shape and size, location and size of the pinnae, haircut, but also microphone locations, hearing aid shells, etc. In practice (so far), the fixed beamformer weights have been estimated, e.g. using a head-and-torso simulator (e.g. Head and Torso Simulator (HATS) 4128C from Brüel & Kjær Sound & Vibration Measurement A/S) by the hearing aid manufacturer and stored in a hearing aid memory. In this sense, the fixed beamformer weights are tailored to the HATS-model, i.e., exhibiting some sort of average human physical characteristics.
The problem is that fixed beamformers implemented like this are close-to-optimal for a HATS-model, but not for a specific hearing aid user. The present disclosure proposes a way for finding the optimal beamformer weights based on the physical appearance of the individual hearing aid user.